//
// Created by whitby on 8/22/23.
//

#include "ORBExtractor.h"
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/features2d/features2d.hpp>

#include <iostream>

using namespace std;

namespace mono_orb_slam2 {
    const int HALF_PATCH_SIZE = 15;
    const int EDGE_THRESHOLD = 19;
    const float factorPI = (float) (CV_PI / 180.f);

    static float IC_Angle(const cv::Mat &image, const cv::Point2f &pt, const vector<int> &u_max) {
        int m_01 = 0, m_10 = 0;

        const uchar *center = &image.at<uchar>(cvRound(pt.y), cvRound(pt.x));

        // Treat the center line differently, v_w=0
        for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
            m_10 += u * center[u];

        // Go line by line in the circular patch
        int step = (int) image.step1();
        for (int v = 1; v <= HALF_PATCH_SIZE; ++v) {
            // Proceed over the two lines
            int v_sum = 0;
            int d = u_max[v];
            for (int u = -d; u <= d; ++u) {
                int val_plus = center[u + v * step], val_minus = center[u - v * step];
                v_sum += (val_plus - val_minus);
                m_10 += u * (val_plus + val_minus);
            }
            m_01 += v * v_sum;
        }

        return cv::fastAtan2((float) m_01, (float) m_10);
    }

    static void computeOrientation(const cv::Mat &image, vector <cv::KeyPoint> &keyPoints, const vector<int> &u_max) {
        for (auto &keyPoint: keyPoints) {
            keyPoint.angle = IC_Angle(image, keyPoint.pt, u_max);
        }
    }

    static void computeOrbDescriptor(const cv::KeyPoint &kpt,
                                     const cv::Mat &img, const cv::Point *pattern,
                                     uchar *desc) {
        float angle = kpt.angle * factorPI;
        float a = cos(angle), b = sin(angle);

        const uchar *center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
        const int step = (int) img.step;


#define GET_VALUE(idx) \
        center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
               cvRound(pattern[idx].x*a - pattern[idx].y*b)]

        //rotation

        for (int i = 0; i < 32; ++i, pattern += 16) {
            int t0, t1, val;
            t0 = GET_VALUE(0);
            t1 = GET_VALUE(1);
            val = t0 < t1;
            t0 = GET_VALUE(2);
            t1 = GET_VALUE(3);
            val |= (t0 < t1) << 1;
            t0 = GET_VALUE(4);
            t1 = GET_VALUE(5);
            val |= (t0 < t1) << 2;
            t0 = GET_VALUE(6);
            t1 = GET_VALUE(7);
            val |= (t0 < t1) << 3;
            t0 = GET_VALUE(8);
            t1 = GET_VALUE(9);
            val |= (t0 < t1) << 4;
            t0 = GET_VALUE(10);
            t1 = GET_VALUE(11);
            val |= (t0 < t1) << 5;
            t0 = GET_VALUE(12);
            t1 = GET_VALUE(13);
            val |= (t0 < t1) << 6;
            t0 = GET_VALUE(14);
            t1 = GET_VALUE(15);
            val |= (t0 < t1) << 7;

            desc[i] = (uchar) val;
        }

#undef GET_VALUE
    }

    static void computeDescriptors(const cv::Mat &image, vector <cv::KeyPoint> &keyPoints, cv::Mat &descriptors,
                                   const vector <cv::Point> &pattern) {
        descriptors = cv::Mat::zeros((int) keyPoints.size(), 32, CV_8UC1);

        for (int i = 0; i < keyPoints.size(); i++) {
            computeOrbDescriptor(keyPoints[i], image, &pattern[0], descriptors.ptr(i));
        }
    }

    static int bit_pattern_31_[256 * 4] = {
            8, -3, 9, 5/*mean (0), correlation (0)*/,
            4, 2, 7, -12/*mean (1.12461e-05), correlation (0.0437584)*/,
            -11, 9, -8, 2/*mean (3.37382e-05), correlation (0.0617409)*/,
            7, -12, 12, -13/*mean (5.62303e-05), correlation (0.0636977)*/,
            2, -13, 2, 12/*mean (0.000134953), correlation (0.085099)*/,
            1, -7, 1, 6/*mean (0.000528565), correlation (0.0857175)*/,
            -2, -10, -2, -4/*mean (0.0188821), correlation (0.0985774)*/,
            -13, -13, -11, -8/*mean (0.0363135), correlation (0.0899616)*/,
            -13, -3, -12, -9/*mean (0.121806), correlation (0.099849)*/,
            10, 4, 11, 9/*mean (0.122065), correlation (0.093285)*/,
            -13, -8, -8, -9/*mean (0.162787), correlation (0.0942748)*/,
            -11, 7, -9, 12/*mean (0.21561), correlation (0.0974438)*/,
            7, 7, 12, 6/*mean (0.160583), correlation (0.130064)*/,
            -4, -5, -3, 0/*mean (0.228171), correlation (0.132998)*/,
            -13, 2, -12, -3/*mean (0.00997526), correlation (0.145926)*/,
            -9, 0, -7, 5/*mean (0.198234), correlation (0.143636)*/,
            12, -6, 12, -1/*mean (0.0676226), correlation (0.16689)*/,
            -3, 6, -2, 12/*mean (0.166847), correlation (0.171682)*/,
            -6, -13, -4, -8/*mean (0.101215), correlation (0.179716)*/,
            11, -13, 12, -8/*mean (0.200641), correlation (0.192279)*/,
            4, 7, 5, 1/*mean (0.205106), correlation (0.186848)*/,
            5, -3, 10, -3/*mean (0.234908), correlation (0.192319)*/,
            3, -7, 6, 12/*mean (0.0709964), correlation (0.210872)*/,
            -8, -7, -6, -2/*mean (0.0939834), correlation (0.212589)*/,
            -2, 11, -1, -10/*mean (0.127778), correlation (0.20866)*/,
            -13, 12, -8, 10/*mean (0.14783), correlation (0.206356)*/,
            -7, 3, -5, -3/*mean (0.182141), correlation (0.198942)*/,
            -4, 2, -3, 7/*mean (0.188237), correlation (0.21384)*/,
            -10, -12, -6, 11/*mean (0.14865), correlation (0.23571)*/,
            5, -12, 6, -7/*mean (0.222312), correlation (0.23324)*/,
            5, -6, 7, -1/*mean (0.229082), correlation (0.23389)*/,
            1, 0, 4, -5/*mean (0.241577), correlation (0.215286)*/,
            9, 11, 11, -13/*mean (0.00338507), correlation (0.251373)*/,
            4, 7, 4, 12/*mean (0.131005), correlation (0.257622)*/,
            2, -1, 4, 4/*mean (0.152755), correlation (0.255205)*/,
            -4, -12, -2, 7/*mean (0.182771), correlation (0.244867)*/,
            -8, -5, -7, -10/*mean (0.186898), correlation (0.23901)*/,
            4, 11, 9, 12/*mean (0.226226), correlation (0.258255)*/,
            0, -8, 1, -13/*mean (0.0897886), correlation (0.274827)*/,
            -13, -2, -8, 2/*mean (0.148774), correlation (0.28065)*/,
            -3, -2, -2, 3/*mean (0.153048), correlation (0.283063)*/,
            -6, 9, -4, -9/*mean (0.169523), correlation (0.278248)*/,
            8, 12, 10, 7/*mean (0.225337), correlation (0.282851)*/,
            0, 9, 1, 3/*mean (0.226687), correlation (0.278734)*/,
            7, -5, 11, -10/*mean (0.00693882), correlation (0.305161)*/,
            -13, -6, -11, 0/*mean (0.0227283), correlation (0.300181)*/,
            10, 7, 12, 1/*mean (0.125517), correlation (0.31089)*/,
            -6, -3, -6, 12/*mean (0.131748), correlation (0.312779)*/,
            10, -9, 12, -4/*mean (0.144827), correlation (0.292797)*/,
            -13, 8, -8, -12/*mean (0.149202), correlation (0.308918)*/,
            -13, 0, -8, -4/*mean (0.160909), correlation (0.310013)*/,
            3, 3, 7, 8/*mean (0.177755), correlation (0.309394)*/,
            5, 7, 10, -7/*mean (0.212337), correlation (0.310315)*/,
            -1, 7, 1, -12/*mean (0.214429), correlation (0.311933)*/,
            3, -10, 5, 6/*mean (0.235807), correlation (0.313104)*/,
            2, -4, 3, -10/*mean (0.00494827), correlation (0.344948)*/,
            -13, 0, -13, 5/*mean (0.0549145), correlation (0.344675)*/,
            -13, -7, -12, 12/*mean (0.103385), correlation (0.342715)*/,
            -13, 3, -11, 8/*mean (0.134222), correlation (0.322922)*/,
            -7, 12, -4, 7/*mean (0.153284), correlation (0.337061)*/,
            6, -10, 12, 8/*mean (0.154881), correlation (0.329257)*/,
            -9, -1, -7, -6/*mean (0.200967), correlation (0.33312)*/,
            -2, -5, 0, 12/*mean (0.201518), correlation (0.340635)*/,
            -12, 5, -7, 5/*mean (0.207805), correlation (0.335631)*/,
            3, -10, 8, -13/*mean (0.224438), correlation (0.34504)*/,
            -7, -7, -4, 5/*mean (0.239361), correlation (0.338053)*/,
            -3, -2, -1, -7/*mean (0.240744), correlation (0.344322)*/,
            2, 9, 5, -11/*mean (0.242949), correlation (0.34145)*/,
            -11, -13, -5, -13/*mean (0.244028), correlation (0.336861)*/,
            -1, 6, 0, -1/*mean (0.247571), correlation (0.343684)*/,
            5, -3, 5, 2/*mean (0.000697256), correlation (0.357265)*/,
            -4, -13, -4, 12/*mean (0.00213675), correlation (0.373827)*/,
            -9, -6, -9, 6/*mean (0.0126856), correlation (0.373938)*/,
            -12, -10, -8, -4/*mean (0.0152497), correlation (0.364237)*/,
            10, 2, 12, -3/*mean (0.0299933), correlation (0.345292)*/,
            7, 12, 12, 12/*mean (0.0307242), correlation (0.366299)*/,
            -7, -13, -6, 5/*mean (0.0534975), correlation (0.368357)*/,
            -4, 9, -3, 4/*mean (0.099865), correlation (0.372276)*/,
            7, -1, 12, 2/*mean (0.117083), correlation (0.364529)*/,
            -7, 6, -5, 1/*mean (0.126125), correlation (0.369606)*/,
            -13, 11, -12, 5/*mean (0.130364), correlation (0.358502)*/,
            -3, 7, -2, -6/*mean (0.131691), correlation (0.375531)*/,
            7, -8, 12, -7/*mean (0.160166), correlation (0.379508)*/,
            -13, -7, -11, -12/*mean (0.167848), correlation (0.353343)*/,
            1, -3, 12, 12/*mean (0.183378), correlation (0.371916)*/,
            2, -6, 3, 0/*mean (0.228711), correlation (0.371761)*/,
            -4, 3, -2, -13/*mean (0.247211), correlation (0.364063)*/,
            -1, -13, 1, 9/*mean (0.249325), correlation (0.378139)*/,
            7, 1, 8, -6/*mean (0.000652272), correlation (0.411682)*/,
            1, -1, 3, 12/*mean (0.00248538), correlation (0.392988)*/,
            9, 1, 12, 6/*mean (0.0206815), correlation (0.386106)*/,
            -1, -9, -1, 3/*mean (0.0364485), correlation (0.410752)*/,
            -13, -13, -10, 5/*mean (0.0376068), correlation (0.398374)*/,
            7, 7, 10, 12/*mean (0.0424202), correlation (0.405663)*/,
            12, -5, 12, 9/*mean (0.0942645), correlation (0.410422)*/,
            6, 3, 7, 11/*mean (0.1074), correlation (0.413224)*/,
            5, -13, 6, 10/*mean (0.109256), correlation (0.408646)*/,
            2, -12, 2, 3/*mean (0.131691), correlation (0.416076)*/,
            3, 8, 4, -6/*mean (0.165081), correlation (0.417569)*/,
            2, 6, 12, -13/*mean (0.171874), correlation (0.408471)*/,
            9, -12, 10, 3/*mean (0.175146), correlation (0.41296)*/,
            -8, 4, -7, 9/*mean (0.183682), correlation (0.402956)*/,
            -11, 12, -4, -6/*mean (0.184672), correlation (0.416125)*/,
            1, 12, 2, -8/*mean (0.191487), correlation (0.386696)*/,
            6, -9, 7, -4/*mean (0.192668), correlation (0.394771)*/,
            2, 3, 3, -2/*mean (0.200157), correlation (0.408303)*/,
            6, 3, 11, 0/*mean (0.204588), correlation (0.411762)*/,
            3, -3, 8, -8/*mean (0.205904), correlation (0.416294)*/,
            7, 8, 9, 3/*mean (0.213237), correlation (0.409306)*/,
            -11, -5, -6, -4/*mean (0.243444), correlation (0.395069)*/,
            -10, 11, -5, 10/*mean (0.247672), correlation (0.413392)*/,
            -5, -8, -3, 12/*mean (0.24774), correlation (0.411416)*/,
            -10, 5, -9, 0/*mean (0.00213675), correlation (0.454003)*/,
            8, -1, 12, -6/*mean (0.0293635), correlation (0.455368)*/,
            4, -6, 6, -11/*mean (0.0404971), correlation (0.457393)*/,
            -10, 12, -8, 7/*mean (0.0481107), correlation (0.448364)*/,
            4, -2, 6, 7/*mean (0.050641), correlation (0.455019)*/,
            -2, 0, -2, 12/*mean (0.0525978), correlation (0.44338)*/,
            -5, -8, -5, 2/*mean (0.0629667), correlation (0.457096)*/,
            7, -6, 10, 12/*mean (0.0653846), correlation (0.445623)*/,
            -9, -13, -8, -8/*mean (0.0858749), correlation (0.449789)*/,
            -5, -13, -5, -2/*mean (0.122402), correlation (0.450201)*/,
            8, -8, 9, -13/*mean (0.125416), correlation (0.453224)*/,
            -9, -11, -9, 0/*mean (0.130128), correlation (0.458724)*/,
            1, -8, 1, -2/*mean (0.132467), correlation (0.440133)*/,
            7, -4, 9, 1/*mean (0.132692), correlation (0.454)*/,
            -2, 1, -1, -4/*mean (0.135695), correlation (0.455739)*/,
            11, -6, 12, -11/*mean (0.142904), correlation (0.446114)*/,
            -12, -9, -6, 4/*mean (0.146165), correlation (0.451473)*/,
            3, 7, 7, 12/*mean (0.147627), correlation (0.456643)*/,
            5, 5, 10, 8/*mean (0.152901), correlation (0.455036)*/,
            0, -4, 2, 8/*mean (0.167083), correlation (0.459315)*/,
            -9, 12, -5, -13/*mean (0.173234), correlation (0.454706)*/,
            0, 7, 2, 12/*mean (0.18312), correlation (0.433855)*/,
            -1, 2, 1, 7/*mean (0.185504), correlation (0.443838)*/,
            5, 11, 7, -9/*mean (0.185706), correlation (0.451123)*/,
            3, 5, 6, -8/*mean (0.188968), correlation (0.455808)*/,
            -13, -4, -8, 9/*mean (0.191667), correlation (0.459128)*/,
            -5, 9, -3, -3/*mean (0.193196), correlation (0.458364)*/,
            -4, -7, -3, -12/*mean (0.196536), correlation (0.455782)*/,
            6, 5, 8, 0/*mean (0.1972), correlation (0.450481)*/,
            -7, 6, -6, 12/*mean (0.199438), correlation (0.458156)*/,
            -13, 6, -5, -2/*mean (0.211224), correlation (0.449548)*/,
            1, -10, 3, 10/*mean (0.211718), correlation (0.440606)*/,
            4, 1, 8, -4/*mean (0.213034), correlation (0.443177)*/,
            -2, -2, 2, -13/*mean (0.234334), correlation (0.455304)*/,
            2, -12, 12, 12/*mean (0.235684), correlation (0.443436)*/,
            -2, -13, 0, -6/*mean (0.237674), correlation (0.452525)*/,
            4, 1, 9, 3/*mean (0.23962), correlation (0.444824)*/,
            -6, -10, -3, -5/*mean (0.248459), correlation (0.439621)*/,
            -3, -13, -1, 1/*mean (0.249505), correlation (0.456666)*/,
            7, 5, 12, -11/*mean (0.00119208), correlation (0.495466)*/,
            4, -2, 5, -7/*mean (0.00372245), correlation (0.484214)*/,
            -13, 9, -9, -5/*mean (0.00741116), correlation (0.499854)*/,
            7, 1, 8, 6/*mean (0.0208952), correlation (0.499773)*/,
            7, -8, 7, 6/*mean (0.0220085), correlation (0.501609)*/,
            -7, -4, -7, 1/*mean (0.0233806), correlation (0.496568)*/,
            -8, 11, -7, -8/*mean (0.0236505), correlation (0.489719)*/,
            -13, 6, -12, -8/*mean (0.0268781), correlation (0.503487)*/,
            2, 4, 3, 9/*mean (0.0323324), correlation (0.501938)*/,
            10, -5, 12, 3/*mean (0.0399235), correlation (0.494029)*/,
            -6, -5, -6, 7/*mean (0.0420153), correlation (0.486579)*/,
            8, -3, 9, -8/*mean (0.0548021), correlation (0.484237)*/,
            2, -12, 2, 8/*mean (0.0616622), correlation (0.496642)*/,
            -11, -2, -10, 3/*mean (0.0627755), correlation (0.498563)*/,
            -12, -13, -7, -9/*mean (0.0829622), correlation (0.495491)*/,
            -11, 0, -10, -5/*mean (0.0843342), correlation (0.487146)*/,
            5, -3, 11, 8/*mean (0.0929937), correlation (0.502315)*/,
            -2, -13, -1, 12/*mean (0.113327), correlation (0.48941)*/,
            -1, -8, 0, 9/*mean (0.132119), correlation (0.467268)*/,
            -13, -11, -12, -5/*mean (0.136269), correlation (0.498771)*/,
            -10, -2, -10, 11/*mean (0.142173), correlation (0.498714)*/,
            -3, 9, -2, -13/*mean (0.144141), correlation (0.491973)*/,
            2, -3, 3, 2/*mean (0.14892), correlation (0.500782)*/,
            -9, -13, -4, 0/*mean (0.150371), correlation (0.498211)*/,
            -4, 6, -3, -10/*mean (0.152159), correlation (0.495547)*/,
            -4, 12, -2, -7/*mean (0.156152), correlation (0.496925)*/,
            -6, -11, -4, 9/*mean (0.15749), correlation (0.499222)*/,
            6, -3, 6, 11/*mean (0.159211), correlation (0.503821)*/,
            -13, 11, -5, 5/*mean (0.162427), correlation (0.501907)*/,
            11, 11, 12, 6/*mean (0.16652), correlation (0.497632)*/,
            7, -5, 12, -2/*mean (0.169141), correlation (0.484474)*/,
            -1, 12, 0, 7/*mean (0.169456), correlation (0.495339)*/,
            -4, -8, -3, -2/*mean (0.171457), correlation (0.487251)*/,
            -7, 1, -6, 7/*mean (0.175), correlation (0.500024)*/,
            -13, -12, -8, -13/*mean (0.175866), correlation (0.497523)*/,
            -7, -2, -6, -8/*mean (0.178273), correlation (0.501854)*/,
            -8, 5, -6, -9/*mean (0.181107), correlation (0.494888)*/,
            -5, -1, -4, 5/*mean (0.190227), correlation (0.482557)*/,
            -13, 7, -8, 10/*mean (0.196739), correlation (0.496503)*/,
            1, 5, 5, -13/*mean (0.19973), correlation (0.499759)*/,
            1, 0, 10, -13/*mean (0.204465), correlation (0.49873)*/,
            9, 12, 10, -1/*mean (0.209334), correlation (0.49063)*/,
            5, -8, 10, -9/*mean (0.211134), correlation (0.503011)*/,
            -1, 11, 1, -13/*mean (0.212), correlation (0.499414)*/,
            -9, -3, -6, 2/*mean (0.212168), correlation (0.480739)*/,
            -1, -10, 1, 12/*mean (0.212731), correlation (0.502523)*/,
            -13, 1, -8, -10/*mean (0.21327), correlation (0.489786)*/,
            8, -11, 10, -6/*mean (0.214159), correlation (0.488246)*/,
            2, -13, 3, -6/*mean (0.216993), correlation (0.50287)*/,
            7, -13, 12, -9/*mean (0.223639), correlation (0.470502)*/,
            -10, -10, -5, -7/*mean (0.224089), correlation (0.500852)*/,
            -10, -8, -8, -13/*mean (0.228666), correlation (0.502629)*/,
            4, -6, 8, 5/*mean (0.22906), correlation (0.498305)*/,
            3, 12, 8, -13/*mean (0.233378), correlation (0.503825)*/,
            -4, 2, -3, -3/*mean (0.234323), correlation (0.476692)*/,
            5, -13, 10, -12/*mean (0.236392), correlation (0.475462)*/,
            4, -13, 5, -1/*mean (0.236842), correlation (0.504132)*/,
            -9, 9, -4, 3/*mean (0.236977), correlation (0.497739)*/,
            0, 3, 3, -9/*mean (0.24314), correlation (0.499398)*/,
            -12, 1, -6, 1/*mean (0.243297), correlation (0.489447)*/,
            3, 2, 4, -8/*mean (0.00155196), correlation (0.553496)*/,
            -10, -10, -10, 9/*mean (0.00239541), correlation (0.54297)*/,
            8, -13, 12, 12/*mean (0.0034413), correlation (0.544361)*/,
            -8, -12, -6, -5/*mean (0.003565), correlation (0.551225)*/,
            2, 2, 3, 7/*mean (0.00835583), correlation (0.55285)*/,
            10, 6, 11, -8/*mean (0.00885065), correlation (0.540913)*/,
            6, 8, 8, -12/*mean (0.0101552), correlation (0.551085)*/,
            -7, 10, -6, 5/*mean (0.0102227), correlation (0.533635)*/,
            -3, -9, -3, 9/*mean (0.0110211), correlation (0.543121)*/,
            -1, -13, -1, 5/*mean (0.0113473), correlation (0.550173)*/,
            -3, -7, -3, 4/*mean (0.0140913), correlation (0.554774)*/,
            -8, -2, -8, 3/*mean (0.017049), correlation (0.55461)*/,
            4, 2, 12, 12/*mean (0.01778), correlation (0.546921)*/,
            2, -5, 3, 11/*mean (0.0224022), correlation (0.549667)*/,
            6, -9, 11, -13/*mean (0.029161), correlation (0.546295)*/,
            3, -1, 7, 12/*mean (0.0303081), correlation (0.548599)*/,
            11, -1, 12, 4/*mean (0.0355151), correlation (0.523943)*/,
            -3, 0, -3, 6/*mean (0.0417904), correlation (0.543395)*/,
            4, -11, 4, 12/*mean (0.0487292), correlation (0.542818)*/,
            2, -4, 2, 1/*mean (0.0575124), correlation (0.554888)*/,
            -10, -6, -8, 1/*mean (0.0594242), correlation (0.544026)*/,
            -13, 7, -11, 1/*mean (0.0597391), correlation (0.550524)*/,
            -13, 12, -11, -13/*mean (0.0608974), correlation (0.55383)*/,
            6, 0, 11, -13/*mean (0.065126), correlation (0.552006)*/,
            0, -1, 1, 4/*mean (0.074224), correlation (0.546372)*/,
            -13, 3, -9, -2/*mean (0.0808592), correlation (0.554875)*/,
            -9, 8, -6, -3/*mean (0.0883378), correlation (0.551178)*/,
            -13, -6, -8, -2/*mean (0.0901035), correlation (0.548446)*/,
            5, -9, 8, 10/*mean (0.0949843), correlation (0.554694)*/,
            2, 7, 3, -9/*mean (0.0994152), correlation (0.550979)*/,
            -1, -6, -1, -1/*mean (0.10045), correlation (0.552714)*/,
            9, 5, 11, -2/*mean (0.100686), correlation (0.552594)*/,
            11, -3, 12, -8/*mean (0.101091), correlation (0.532394)*/,
            3, 0, 3, 5/*mean (0.101147), correlation (0.525576)*/,
            -1, 4, 0, 10/*mean (0.105263), correlation (0.531498)*/,
            3, -6, 4, 5/*mean (0.110785), correlation (0.540491)*/,
            -13, 0, -10, 5/*mean (0.112798), correlation (0.536582)*/,
            5, 8, 12, 11/*mean (0.114181), correlation (0.555793)*/,
            8, 9, 9, -6/*mean (0.117431), correlation (0.553763)*/,
            7, -4, 8, -12/*mean (0.118522), correlation (0.553452)*/,
            -10, 4, -10, 9/*mean (0.12094), correlation (0.554785)*/,
            7, 3, 12, 4/*mean (0.122582), correlation (0.555825)*/,
            9, -7, 10, -2/*mean (0.124978), correlation (0.549846)*/,
            7, 0, 12, -2/*mean (0.127002), correlation (0.537452)*/,
            -1, -6, 0, -11/*mean (0.127148), correlation (0.547401)*/
    };

    void ExtractorNode::DivideNode(ExtractorNode &n1, ExtractorNode &n2, ExtractorNode &n3, ExtractorNode &n4) {
        const int halfX = (UR.x - UL.x) / 2;
        const int halfY = (BL.y - UL.y) / 2;

        // Define boundaries of child
        n1.UL = UL;
        n1.UR = cv::Point2i(UL.x + halfX, UL.y);
        n1.BL = cv::Point2i(UL.x, UL.y + halfY);
        n1.BR = cv::Point2i(UL.x + halfX, UL.y + halfY);
        n1.key_points.reserve(key_points.size());

        n2.UL = n1.UR;
        n2.UR = UR;
        n2.BL = n1.BR;
        n2.BR = cv::Point2i(UR.x, UL.y + halfY);
        n2.key_points.reserve(key_points.size());

        n3.UL = n1.BL;
        n3.UR = n1.BR;
        n3.BL = BL;
        n3.BR = cv::Point2i(n1.BR.x, BL.y);
        n3.key_points.reserve(key_points.size());

        n4.UL = n3.UR;
        n4.UR = n2.BR;
        n4.BL = n3.BR;
        n4.BR = BR;
        n4.key_points.reserve(key_points.size());

        // Associate points to child
        for (auto &kp: key_points) {
            if (cvFloor(kp.pt.x) < n1.UR.x) {
                if (cvFloor(kp.pt.y) < n1.BR.y)
                    n1.key_points.push_back(kp);
                else
                    n3.key_points.push_back(kp);
            } else if (cvFloor(kp.pt.y) < n1.BR.y)
                n2.key_points.push_back(kp);
            else
                n4.key_points.push_back(kp);
        }

        if (n1.key_points.size() == 1) n1.beNoMore = true;
        if (n2.key_points.size() == 1) n2.beNoMore = true;
        if (n3.key_points.size() == 1) n3.beNoMore = true;
        if (n4.key_points.size() == 1) n4.beNoMore = true;
    }

    /// ORBExtractor
    float ORBExtractor::scale_factor = 1.f;
    float ORBExtractor::log_sale_factor = 1.f;
    int ORBExtractor::n_levels = 1;
    vector<float> ORBExtractor::scale_factors;
    vector<float> ORBExtractor::inv_scale_factors;
    vector<float> ORBExtractor::square_sigmas;
    vector<float> ORBExtractor::inv_square_sigmas;

    ORBExtractor::ORBExtractor(int nFeatures, float scaleFactor, int nLevels, int iniThFast, int minThFast)
            : n_features(nFeatures), ini_th_fast(iniThFast), min_th_fast(minThFast) {
        // initialize pyramid information
        scale_factor = scaleFactor;
        log_sale_factor = log(scaleFactor);
        n_levels = nLevels;
        scale_factors.resize(n_levels), inv_scale_factors.resize(n_levels);
        square_sigmas.resize(n_levels), inv_square_sigmas.resize(n_levels);
        scale_factors[0] = 1.f, inv_scale_factors[0] = 1.f, square_sigmas[0] = 1.f, inv_square_sigmas[0] = 1.f;
        for (int i = 1; i < n_levels; i++) {
            scale_factors[i] = scale_factors[i - 1] * scale_factor;
            inv_scale_factors[i] = 1.f / scale_factors[i];
            square_sigmas[i] = scale_factors[i] * scale_factors[i];
            inv_square_sigmas[i] = 1.f / square_sigmas[i];
        }

        image_pyramid.resize(n_levels);

        // compute the num of features at every level
        n_features_per_level.resize(n_levels);
        float invScaleFactor2 = 1.0f / (scale_factor * scale_factor);
        float nDesiredFeaturesPerScale = n_features * (1 - invScaleFactor2) / (1 - pow(invScaleFactor2, n_levels));
        int sumFeatures = 0;
        for (int level = 0; level < n_levels - 1; level++) {
            n_features_per_level[level] = cvRound(nDesiredFeaturesPerScale);
            sumFeatures += n_features_per_level[level];
            nDesiredFeaturesPerScale *= invScaleFactor2;
        }
        n_features_per_level[n_levels - 1] = std::max(n_features - sumFeatures, 1);

        const int nPoints = 512;
        const auto *pattern0 = (const cv::Point *) bit_pattern_31_;
        std::copy(pattern0, pattern0 + nPoints, std::back_inserter(pattern));

        //This is for orientation
        // pre-compute the end of a row in a circular patch
        u_max.resize(HALF_PATCH_SIZE + 1);

        int v, v0, vMax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
        int vMin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
        const double hp2 = HALF_PATCH_SIZE * HALF_PATCH_SIZE;
        for (v = 0; v <= vMax; ++v)
            u_max[v] = cvRound(sqrt(hp2 - v * v));

        // Make sure we are symmetric
        for (v = HALF_PATCH_SIZE, v0 = 0; v >= vMin; --v) {
            while (u_max[v0] == u_max[v0 + 1])
                ++v0;
            u_max[v] = v0;
            ++v0;
        }
    }

    ORBExtractor::ORBExtractor(int nFeatures, const ORBExtractor &orbExtractor)
            : n_features(nFeatures), pattern(orbExtractor.pattern), u_max(orbExtractor.u_max),
              ini_th_fast(orbExtractor.ini_th_fast), min_th_fast(orbExtractor.min_th_fast) {
        image_pyramid.resize(n_levels);

        // compute the num of features at every level
        n_features_per_level.resize(n_levels);
        float invScaleFactor2 = 1.0f / (scale_factor * scale_factor);
        float nDesiredFeaturesPerScale = n_features * (1 - invScaleFactor2) / (1 - pow(invScaleFactor2, n_levels));
        int sumFeatures = 0;
        for (int level = 0; level < n_levels - 1; level++) {
            n_features_per_level[level] = cvRound(nDesiredFeaturesPerScale);
            sumFeatures += n_features_per_level[level];
            nDesiredFeaturesPerScale *= invScaleFactor2;
        }
        n_features_per_level[n_levels - 1] = std::max(n_features - sumFeatures, 1);
    }

    void ORBExtractor::operator()(const cv::Mat &image, std::vector<cv::KeyPoint> &_keyPoints,
                                  cv::Mat &descriptors) {
        if (image.empty()) return;

        assert(image.type() == CV_8UC1);

        // Pre-compute the scale pyramids
        ComputePyramid(image);

        vector<vector<cv::KeyPoint>> allKeyPoints;
        ComputeKeyPointsOctTree(allKeyPoints);

        int numKeyPoints = 0;
        for (int level = 0; level < n_levels; ++level) {
            numKeyPoints += (int) allKeyPoints[level].size();
        }

        if (numKeyPoints == 0) return;
        else {
            descriptors.create(numKeyPoints, 32, CV_8U);
        }

        _keyPoints.clear();
        _keyPoints.reserve(numKeyPoints);

        int offset = 0;
        for (int level = 0; level < n_levels; ++level) {
            vector<cv::KeyPoint> &keyPoints = allKeyPoints[level];
            if (keyPoints.empty()) continue;
            int numKeyPointsLevel = static_cast<int>(keyPoints.size());

            // preprocess the resized image
            cv::Mat workingMat = image_pyramid[level].clone();
            cv::GaussianBlur(workingMat, workingMat, cv::Size(7, 7), 2, 2, cv::BORDER_REFLECT_101);

            // compute the descriptors
            cv::Mat desc = descriptors.rowRange(offset, offset + numKeyPointsLevel);
            computeDescriptors(workingMat, keyPoints, desc, pattern);

            offset += numKeyPointsLevel;

            // Scale key-point coordinates
            if (level != 0) {
                float scale = scale_factors[level];
                for (auto &keyPoint: keyPoints) {
                    keyPoint.pt *= scale;
                }
            }

            // And add the key-points to the output
            _keyPoints.insert(_keyPoints.end(), keyPoints.begin(), keyPoints.end());
        }
    }

    void ORBExtractor::print() const {
        cout << endl << "ORB Pyramid Information: " << endl;
        cout << " - Features: " << n_features << "(at initial stage)" << endl;
        cout << " - ScaleFactor: " << scale_factor << endl;
        cout << " - Levels: " << n_levels << endl;
        cout << " - IniThFAST: " << ini_th_fast << endl;
        cout << " - MinThFAST: " << min_th_fast << endl;
        cout << endl;
    }

    void ORBExtractor::ComputePyramid(const cv::Mat &image) {
        for (int level = 0; level < n_levels; ++level) {
            // Compute the resized image
            if (level != 0) {
                float scale = inv_scale_factors[level];
                cv::Size sz(cvRound((float) image.cols * scale), cvRound((float) image.rows * scale));
                resize(image_pyramid[level - 1], image_pyramid[level], sz, 0, 0, cv::INTER_LINEAR);
            } else {
                image_pyramid[level] = image.clone();
            }
        }
    }

    void ORBExtractor::ComputeKeyPointsOctTree(std::vector<std::vector<cv::KeyPoint>> &allKeyPoints) {
        allKeyPoints.resize(n_levels);

        const int W = 30;

        for (int level = 0; level < n_levels; ++level) {
            const int minBorderX = EDGE_THRESHOLD;
            const int minBorderY = minBorderX;
            const int maxBorderX = image_pyramid[level].cols - EDGE_THRESHOLD;
            const int maxBorderY = image_pyramid[level].rows - EDGE_THRESHOLD;

            vector<cv::KeyPoint> vecToDistributeKPs;
            vecToDistributeKPs.reserve(n_features_per_level[level] * 10);

            const int width = (maxBorderX - minBorderX);
            const int height = (maxBorderY - minBorderY);

            const int nCols = width % W == 0 ? width / W : width / W + 1;
            const int nRows = height % W == 0 ? height / W : height / W + 1;

            for (int i = 0; i < nRows; ++i) {
                const int iniY = minBorderY + i * W;
                int maxY = min(iniY + W, maxBorderY);

                for (int j = 0; j < nCols; ++j) {
                    const int iniX = minBorderX + j * W;
                    int maxX = min(iniX + W, maxBorderX);

                    vector<cv::KeyPoint> cellKeyPoints;
                    cv::FAST(image_pyramid[level].rowRange(iniY - 3, maxY + 3).colRange(iniX - 3, maxX + 3),
                             cellKeyPoints, ini_th_fast, true);

                    if (cellKeyPoints.empty()) {
                        cv::FAST(image_pyramid[level].rowRange(iniY - 3, maxY + 3).colRange(iniX - 3, maxX + 3),
                                 cellKeyPoints, min_th_fast, true);
                    }

                    if (!cellKeyPoints.empty()) {
                        for (auto &kp: cellKeyPoints) {
                            kp.pt.x += static_cast<float>(j * W - 3);
                            kp.pt.y += static_cast<float>(i * W - 3);
                            vecToDistributeKPs.push_back(kp);
                        }
                    }
                }
            }

            vector<cv::KeyPoint> &keyPoints = allKeyPoints[level];
            keyPoints.reserve(n_features_per_level[level]);

            keyPoints = DistributeOctree(vecToDistributeKPs, minBorderX, maxBorderX, minBorderY, maxBorderY,
                                         n_features_per_level[level]);

            // Add border to coordinates and scale information
            for (auto &kp: keyPoints) {
                kp.pt.x += minBorderX;
                kp.pt.y += minBorderY;

                kp.octave = level;
                kp.size = scale_factors[level];
            }
        }

        // compute orientations
        for (int level = 0; level < n_levels; ++level)
            computeOrientation(image_pyramid[level], allKeyPoints[level], u_max);
    }

    vector <cv::KeyPoint>
    ORBExtractor::DistributeOctree(const std::vector<cv::KeyPoint> &vecToDistributeKPs, const int &minX,
                                   const int &maxX, const int &minY, const int &maxY, const int &nFeatures) {
        // Compute the number of initial nodes

        const int nIni = cvCeil(float(maxX - minX) / float(maxY - minY));
        const int hX = cvCeil(float(maxX - minX) / float(nIni));

        list<ExtractorNode> listNodes;
        vector<ExtractorNode *> ptrIniNodes;
        ptrIniNodes.resize(nIni);

        for (int i = 0; i < nIni - 1; ++i) {
            ExtractorNode ni;
            ni.UL = cv::Point2i(hX * i, 0);
            ni.UR = cv::Point2i(hX * (i + 1), 0);
            ni.BL = cv::Point2i(hX * i, maxY - minY);
            ni.BR = cv::Point2i(hX * (i + 1), maxY - minY);

            listNodes.push_back(ni);
            ptrIniNodes[i] = &listNodes.back();
        }
        {
            ExtractorNode ni;
            ni.UL = cv::Point2i(hX * (nIni - 1), 0);
            ni.UR = cv::Point2i(maxX, 0);
            ni.BL = cv::Point2i(hX * (nIni - 1), maxY - minY);
            ni.BR = cv::Point2i(maxX, maxY - minY);
            listNodes.push_back(ni);
            ptrIniNodes[nIni - 1] = &listNodes.back();
        }

        // Associate points to child
        for (auto &kp: vecToDistributeKPs) {
            ptrIniNodes[cvFloor(kp.pt.x) / hX]->key_points.push_back(kp);
        }

        auto iterNode = listNodes.begin();
        while (iterNode != listNodes.end()) {
            if (iterNode->key_points.size() == 1) {
                iterNode->beNoMore = true;
                iterNode++;
            } else if (iterNode->key_points.empty())
                iterNode = listNodes.erase(iterNode);
            else
                iterNode++;
        }

        bool beFinish = false;
        vector<pair<int, ExtractorNode *>> vecSizeAndPtrNode;
        vecSizeAndPtrNode.reserve(listNodes.size() * 4);

        while (!beFinish) {
            size_t preSize = listNodes.size();
            iterNode = listNodes.begin();
            int nToExpand = 0;
            vecSizeAndPtrNode.clear();

            while (iterNode != listNodes.end()) {
                if (iterNode->beNoMore) {
                    iterNode++;
                    continue;
                } else {
                    ExtractorNode n1, n2, n3, n4;
                    iterNode->DivideNode(n1, n2, n3, n4);

                    // Add child if they contain points
                    if (!n1.key_points.empty()) {
                        listNodes.push_front(n1);
                        if (n1.key_points.size() > 1) {
                            nToExpand++;
                            vecSizeAndPtrNode.emplace_back(n1.key_points.size(), &listNodes.front());
                            listNodes.front().iter = listNodes.begin();
                        }
                    }

                    if (!n2.key_points.empty()) {
                        listNodes.push_front(n2);
                        if (n2.key_points.size() > 1) {
                            nToExpand++;
                            vecSizeAndPtrNode.emplace_back(n2.key_points.size(), &listNodes.front());
                            listNodes.front().iter = listNodes.begin();
                        }
                    }

                    if (!n3.key_points.empty()) {
                        listNodes.push_front(n3);
                        if (n3.key_points.size() > 1) {
                            nToExpand++;
                            vecSizeAndPtrNode.emplace_back(n3.key_points.size(), &listNodes.front());
                            listNodes.front().iter = listNodes.begin();
                        }
                    }

                    if (!n4.key_points.empty()) {
                        listNodes.push_front(n4);
                        if (n4.key_points.size() > 1) {
                            nToExpand++;
                            vecSizeAndPtrNode.emplace_back(n4.key_points.size(), &listNodes.front());
                            listNodes.front().iter = listNodes.begin();
                        }
                    }

                    iterNode = listNodes.erase(iterNode);
                    continue;
                }
            }

            // Finish if there are more nodes than required features
            // or all nodes contain just one point
            if (listNodes.size() > nFeatures || listNodes.size() == preSize)
                beFinish = true;
            else if (listNodes.size() + nToExpand * 3 > nFeatures) {
                while (!beFinish) {
                    preSize = listNodes.size();
                    vector<pair<int, ExtractorNode *>> vecPreSizeAndPtrNode = vecSizeAndPtrNode;
                    vecSizeAndPtrNode.clear();
                    sort(vecPreSizeAndPtrNode.begin(), vecPreSizeAndPtrNode.end());

                    for (auto &pair: vecPreSizeAndPtrNode) {
                        ExtractorNode n1, n2, n3, n4;
                        pair.second->DivideNode(n1, n2, n3, n4);

                        // Add child if they contain points
                        if (!n1.key_points.empty()) {
                            listNodes.push_front(n1);
                            if (n1.key_points.size() > 1) {
                                nToExpand++;
                                vecSizeAndPtrNode.emplace_back(n1.key_points.size(), &listNodes.front());
                                listNodes.front().iter = listNodes.begin();
                            }
                        }

                        if (!n2.key_points.empty()) {
                            listNodes.push_front(n2);
                            if (n2.key_points.size() > 1) {
                                nToExpand++;
                                vecSizeAndPtrNode.emplace_back(n2.key_points.size(), &listNodes.front());
                                listNodes.front().iter = listNodes.begin();
                            }
                        }

                        if (!n3.key_points.empty()) {
                            listNodes.push_front(n3);
                            if (n3.key_points.size() > 1) {
                                nToExpand++;
                                vecSizeAndPtrNode.emplace_back(n3.key_points.size(), &listNodes.front());
                                listNodes.front().iter = listNodes.begin();
                            }
                        }

                        if (!n4.key_points.empty()) {
                            listNodes.push_front(n4);
                            if (n4.key_points.size() > 1) {
                                nToExpand++;
                                vecSizeAndPtrNode.emplace_back(n4.key_points.size(), &listNodes.front());
                                listNodes.front().iter = listNodes.begin();
                            }
                        }

                        listNodes.erase(pair.second->iter);

                        if (listNodes.size() >= nFeatures)
                            break;
                    }

                    if (listNodes.size() >= nFeatures || listNodes.size() == preSize)
                        beFinish = true;
                }
            }
        }

        // Retain the best point in each node
        vector<cv::KeyPoint> resultKeyPoints;
        resultKeyPoints.reserve(nFeatures);
        for (auto &node: listNodes) {
            vector<cv::KeyPoint> &nodeKPs = node.key_points;
            cv::KeyPoint *bestKP = &nodeKPs[0];
            float bestResponse = bestKP->response;
            for (size_t k = 1; k < nodeKPs.size(); ++k) {
                if (nodeKPs[k].response > bestResponse) {
                    bestKP = &nodeKPs[k];
                    bestResponse = nodeKPs[k].response;
                }
            }

            resultKeyPoints.push_back(*bestKP);
        }

        return resultKeyPoints;
    }
} // mono_orb_slam2